This document provides an overview of Bayesian learning methods. It discusses key concepts like Bayes' theorem, maximum a posteriori hypotheses, and maximum likelihood hypotheses. Bayes' theorem allows calculating the posterior probability of a hypothesis given observed data and prior probabilities. The maximum a posteriori hypothesis is the one with the highest posterior probability. Maximum likelihood hypotheses maximize the likelihood of the data. Bayesian learning faces challenges from requiring many initial probabilities and high computational costs but provides a useful perspective on machine learning algorithms.

1. UNIT II
Bayesian Learning:

2. Contents
Bayes Theorem –Concept Learning – Maximum Likelihood Hypothesis for
Predicting Probabilities – Minimal Description Length Principle – Bayes
Optimal Classifier – Gibbs Algorithm – Naïve Bayes Classifier– Example –
Bayesian Belief Network – EM Algorithm.

3. BAYESIAN LEARNING-Introduction
● Bayesian reasoning provides a probabilistic approach to inference
● Bayesian reasoning provides the basis for learning algorithms that directly
manipulate probabilities
● Bayesian learning methods are relevant to our study of machine learning for two
different reasons
○ First, Bayesian learning algorithms that calculate explicit probabilities for
hypotheses
○ They provide a useful perspective for understanding many learning algorithms
that do not explicitly manipulate probabilities
A basic familiarity with Bayesian methods is important to understanding and
characterizing the operation of many algorithms in machine learning.

4. Features of Bayesian learning methods include:
● Each observed training example can incrementally decrease or increase the estimated
probability that a hypothesis is correct.
● Prior knowledge can be combined with observed data to determine the final probability
a hypothesis. In Bayesian learning, prior knowledge is provided by asserting
(1) a prior probability for each candidate hypothesis, and
(2) a probability distribution over observed data for each possible hypothesis.
● Bayesian methods can accommodate hypotheses that make probabilistic predictions
● New instances can be classified by combining the predictions of multiple hypotheses,
weighted by their probabilities.
● Even in cases where Bayesian methods prove computationally intractable, they can
provide a standard of optimal decision making against which other practical methods can
be measured

5. Practical difficulty in applying Bayesian methods
● They typically require initial knowledge of many probabilities.
● Significant computational cost required to determine the Bayes optimal
hypothesis in the general case

6. BAYES THEOREM
● In machine learning we are often interested in determining the best
hypothesis from some space H, given the observed training data D.
● One way to specify what we mean by the best hypothesis is to say that we
demand the most probable hypothesis, given the data D plus any initial
knowledge about the prior probabilities of the various hypotheses in H.
● Bayes theorem provides a way to
○ calculate the probability of a hypothesis based on its prior probability,
○ the probabilities of observing various data given the hypothesis,(P(D|h))
and
○ the observed data itself.(p(D))

7. Notations
● P(h) - denote the initial probability that hypothesis h holds, before
we have observed the training data.
○ P(h) is often called the prior probability of h
● P(D) - denote the prior probability that training data D will be
observed
● P(D|h) to denote the probability of observing data D given some
world in which hypothesis h holds.
● P (h|D) that h holds given the observed training data D.
○ P (h|D) is called the posterior probability of h,

8. Bayes theorem:
It provides a way to calculate the posterior probability P(h/D), from the
prior probability P(h), together with P(D) and P(D/h).

9. MAP-Maximum a posteriori hypothesis
● In many learning scenarios, the learner considers some set of
candidate hypotheses H and is interested in finding the most
probable hypothesis h E H given the observed data D
● Any such maximally probable hypothesis is called a maximum a
posteriori (MAP) hypothesis.

10. ● We can determine the MAP hypotheses by using Bayes theorem to
calculate the posterior probability of each candidate hypothesis
Further simplify the equation

11. P(D/h) is often called the likelihood of the data D given h, and any hypothesis
that maximizes P(D/h) is called a maximum likelihood (ML) hypothesis, hML

12. Example:
● To illustrate Bayes rule, consider a medical diagnosis problem in which
there are two alternative hypotheses:
(1) that the patient has a particular form of cancer. and
(2) that the patient does not.
● The available data is from a particular laboratory test with two
possible outcomes: (positive) and (negative).
● We have prior knowledge that over the entire population of people
only .008 have this disease
● Furthermore, the lab test is only an imperfect indicator of the
disease

13. ● The test returns a correct positive result in only 98% of the cases in
which the disease is actually present
● The test returns a correct negative result in only 97% of the cases in
which the disease is not present.
● In other cases, the test returns the opposite result.
● The above situation can be summarized by the following probabilities:

14. ● Suppose we now observe a new patient for whom the lab test returns a
positive result. (check the result with MAP)
● Should we diagnose the patient as having cancer or not?
● The maximum a posteriori hypothesis can be found (h map)
hmap=? (what is your inference) ⇒ patient does not have cancer
The lab test is diagnosed as positive, but the person is not having cancer(
after MAP result )
High probability value for
negative

15. BAYES THEOREM AND CONCEPT LEARNING
What is the relationship between Bayes theorem and the problem of concept learning?
● Concept learning that makes the machine to learn some concepts by providing the
training data and it can generalize well to predict the unseen data
● Bayes theorem calculates the Posterior probability that can be used as the basis
for a straightforward learning algorithm that calculates the probability for each
possible hypothesis, then outputs the most probable.

16. Basic Probability formulas

17. 1.Brute-Force Bayes Concept Learning
● We can design a straightforward concept learning algorithm to output the
maximum a posteriori hypothesis, based on Bayes theorem, as follows:

18. ● In order to specify a Learning problem for the BRUTE-FORCE MAP LEARNING
algorithm we must specify what values are to be used for P(h) and for P(D|h)
● Let us choose P(h) and P(D|h) to be consistent with the following assumptions:
● Given these assumptions, what values should we specify for P(h)?

19. Given these assumptions, what values should we specify for P(h)?
● Given no prior knowledge that one hypothesis is more likely than another, it is
reasonable to assign the same prior probability to every hypothesis h in H.
● Furthermore, because we assume the target concept is contained in H we should
require that these prior probabilities sum to 1.
● Together these constraints imply that we should choose

20. What choice shall we make for P(D|h)?
● P(D|h) is the probability of observing the target values D = (d1 . . .dm) for the
fixed set of instances (x1 . . . xm,),given a world in which hypothesis h holds
● Let us consider the first step of this algorithm, which uses Bayes theorem to
compute the posterior probability P(h|D) of each hypothesis h given the observed
training data D.

21. ● Recalling Bayes theorem, we have
First case(h is inconsistent with D)
● First consider the case where h is inconsistent with the training data D. we can
define P(D|h) to be 0 when h is inconsistent with D
● The posterior probability of a hypothesis inconsistent with D is zero.

22. ● Recalling Bayes theorem, we have
Second case((h is consistent with D)
● Now consider the case where h is consistent with D. we can define P(Dlh) to be 1
when h is consistent with D, we have
Substitute P(D) here

23. ● To summarize, Bayes theorem implies that the posterior probability P(h|D) under
our assumed P(h) and P(D|h) is

24. The evolution of probabilities associated with hypotheses
● Initially all hypotheses have the same probability.(6.1a) As training data accumulates
(Figures 6.1 b and 6. 1 c), the posterior probability for inconsistent hypotheses becomes
zero while the total probability summing to one is shared equally among the remaining
consistent hypotheses.
Every consistent
hypothesis is,
therefore,
a MAP hypothesis.

25. 2.MAP Hypotheses and Consistent Learners
● Every hypothesis consistent with D is a MAP hypothesis.
● We will say that a learning algorithm is a consistent learner provided it
outputs a hypothesis that commits zero errors over the training examples
● Given the above analysis, we can conclude that every consistent learner
outputs a MAP hypothesis, if we assume a uniform prior probability
distribution over H (i.e., P(hi) = P(hj) for all i, j), and if we assume
deterministic, noise free training data (i.e., P(D|h) = 1 if D and h are
consistent, and 0 otherwise).

26. MAXIMUM LIKELIHOOD HYPOTHESES FOR PREDICTING
PROBABILITIES
● Consider the setting in which we wish to learn a nondeterministic (probabilistic)
function f : X -> {0, 1} which has two discrete output values
● For example, the instance space X might represent medical patients in terms of
their symptoms, and the target function f (x) might be 1 if the patient survives
the disease and 0 if not.
● For example, among a collection of patients exhibiting the same set of observable
symptoms, we might find that 92% survive, and 8% do not
● This unpredictability could arise from our inability to observe all the important
distinguishing features of the patients,
● Given this problem setting, we might wish to learn a neural network (or other real-
valued function approximator) whose output is the probability that f (x) = 1.

27. ● In other words, we seek to learn the target function, f' : X -> [O, 1] such that
f '(x) = P( f (x) = 1).
● In the above medical patient example, if x is one of those indistinguishable
patients of which 92% survive, then f'(x) = 0.92 whereas the probabilistic function
f (x) will be equal to 1 in 92% of cases and equal to 0 in the remaining 8%.
What criterion should we optimize in order to find a maximum likelihood hypothesis
for f' in this setting?
● We must first obtain an expression for P(D|h).
● Let us assume the training data D is of the form D = {(x1, d1) . . . (xm, dm)}, where
di is the observed 0 or 1 value for f (xi).

28. ● Treating both xi and di as random variables, and assuming that each training
example is drawn independently, we can write P(D|h) as
● It is reasonable to assume, furthermore, that the probability of encountering any
particular instance xi is independent of the hypothesis h.
● When x is independent of h we can rewrite the above expression
(1)

29. ● Now what is the probability P(di|h, xi) of observing di = 1 for a single
instance xi, given a world in which hypothesis h holds?
● h is our hypothesis regarding the target function, which computes this very
probability. Therefore, P(di = 1 | h, xi) = h(xi), and in general
(2)
● In order to substitute this into the Equation (1) for P(Dlh), let us first " re-
express it in a more mathematically manipulable form, as
(3)

30. Let us use the 3rd equation to substitute for P(di|h, xi) in the following equation
Now we write an expression for the maximum likelihood hypothesis
The last term is a constant independent of h, so it can be dropped
The expression on the right side of above Equation can be seen as a generalization of
the Binomial distribution

31. As in earlier cases, we will find it easier to work with the log of the likelihood, yielding
The above equation describes the quantity that must be maximized in order to obtain
the maximum likelihood hypothesis in our current problem setting.
This result is analogous to our earlier result showing that minimizing the sum of
squared errors produces the maximum likelihood hypothesis in the earlier problem
setting

32. MINIMUM DESCRIPTION LENGTH PRINCIPLE
● Occam's razor, a popular inductive bias that can be summarized as "choose the
shortest explanation for the observed data."
● Consider a Bayesian perspective on this issue and a closely related principle called
the Minimum Description Length (MDL) principle.
● The Minimum Description Length principle is motivated by interpreting the
definition of hMAP the light of basic concepts from information theory.
● Consider again the now familiar definition of MAP.

33. ● which can be equivalently expressed in terms of maximizing the log,
● or alternatively, minimizing the negative of this quantity
● It can be can be interpreted as a statement that short hypotheses are preferred
● Consider the problem of designing a code to transmit messages drawn at random,
where the probability of encountering message i is pi.
● We are interested here in the most compact code; that is, we are interested in
the code that minimizes the expected number of bits we must transmit in order to
encode a message drawn at random

34. ● To minimize the expected code length we should assign shorter codes to messages
that are more probable
● Shannon and Weaver (1949) showed that the optimal code (i.e., the code that
minimizes the expected message length) assigns - log2 pi bits+ to encode message i
● We will refer to the number of bits required to encode message i using code C as
the description length of message i with respect to C, which we denote by Lc(i).
● Let us interpret this Equation in light of the above result from coding theory.
● - log2 P(h) is the description length of h under the optimal encoding for the
hypothesis space H. In other words, this is the size of the description of
hypothesis h using this optimal representation. In our notation, LCH (h) = - log2
P(h), where CH is the optimal code for hypothesis space H.

35. ● -log2 P(D|h) is the description length of the training data D given hypothesis h,
under its optimal encoding. In our notation, LcD|h(Dlh) = - log2P(Dlh), where CD|h
is the optimal code for describing data D assuming that both the sender and
receiver know the hypothesis h.
● Therefore we can rewrite the above equation to show that hMAP is the hypothesis
h that minimizes the sum given by the description length of the hypothesis plus
the description length of the data given the hypothesis
● where CH and CDlh are the optimal encodings for H and for D given h, respectively.
● The Minimum Description Length (MDL) principle recommends choosing the
hypothesis that minimizes the sum of these two description lengths

36. ● Assuming we use the codes C1 and C2 to represent the hypothesis and the data
given the hypothesis, we can state the MDL principle as
The above analysis shows that if we choose C1 to be the optimal encoding of hypotheses
CH, and if we choose C2 to be the optimal encoding CDlh, then
hMDL= h MAP.
Thus the MDL principle provides a way of trading off hypothesis complexity for the
number of errors committed by the hypothesis.
It might select a shorter hypothesis that makes a few errors over a longer hypothesis

37. BAYES OPTIMAL CLASSIFIER
● Till now we are dealing with “most probable hypothesis given the training data”
● Next target is “To find the most probable classification of the new instance given
the training data”
● It can be done by simply applying the “MAP hypothesis” to the new instance, in
fact it is possible to do better.
● Consider a hypothesis space containing three hypotheses, h1, h2, and h3. Suppose
that the posterior probabilities of these hypotheses given the training data are .4,
.3, and .3 respectively.
● Thus, h1 is the MAP hypothesis.
● Suppose a new instance x is encountered, which is classified positive by h1, but
negative by h2 and h3. Taking all hypotheses into account,the probability that x is
positive is .4 (the probability associated with h1), and

38. ● Taking all hypotheses into account,
○ The probability that x is positive is .4 (the probability associated with h1)
○ The probability that it is negative is therefore .6.
● The most probable classification (negative) in this case is different from the
classification generated by the MAP hypothesis.
● In general, the most probable classification of the new instance is obtained by
combining the predictions of all hypotheses, weighted by their posterior
probabilities.
● If the possible classification of the new example can take on any value vj from
some set V, then the probability P(vjlD) that the correct classification for the new
instance is vj is just

39. ● The optimal classification of the new instance is the value vj, for which P (vj|D) is
maximum.
(1)

41. ● Any system that classifies new instances according to the below Equation is called
a Bayes optimal classifier, or Bayes optimal learner
This method maximizes the probability that the new instance is classified correctly,
given the available data, hypothesis space, and prior probabilities over the hypotheses

42. GIBBS ALGORITHM
● Although the Bayes optimal classifier obtains the best performance that can be
achieved from the given training data, it can be quite costly to apply (expensive if
there are many hypotheses)
● The expense is due to the fact that it computes the posterior probability for
every hypothesis in H and then combines the predictions of each hypothesis to
classify each new instance.
● An alternative, less optimal method is the Gibbs algorithm defined as follows:
1. Choose a hypothesis h from H at random, according to the posterior probability
distribution over H.
2. Use h to predict the classification of the next instance x.

43. Surprising fact:
● If the target concept is selected randomly according to the P(h|D) distribution,
then the expected error of Gibbs classifier is at worst twice the expected value
of the error of the Bayes optimal classifier.

44. NAIVE BAYES CLASSIFIER
● One highly practical Bayesian learning method is the naive Bayes learner, often
called the naive Bayes classifier
● In some domains its performance has been shown to be comparable to that of
neural network and decision tree learning
● The naive Bayes classifier applies to learning tasks where each instance x is
described by a conjunction of attribute values and where the target function f ( x
) can take on any value from some finite set V.
● A set of training examples of the target function is provided, and a new instance is
presented, described by the tuple of attribute values (a1, a2.. .an,).
● The learner is asked to predict the target value, or classification, for this new
instance.

45. ● The Bayesian approach to classifying the new instance is to assign the most
probable target value VMAP, given the attribute values (a1,a2 . . .an ,) that
describe the instance.
(1)

46. ● Now we could attempt to estimate the two terms based on the training data.
● It is easy to estimate each of the P(vj) simply by counting the frequency with
which each target value vj occurs in the training data.
● The naive Bayes classifier is based on the simplifying assumption that the
attribute values are conditionally independent given the target value.
● In other words, the assumption is that given the target value of the instance, the
probability of observing the conjunction a1, a2.. .an, is just the product of the
probabilities for the individual attributes:

47. ● Substituting this into Equation 1, we have the approach used by the naive Bayes
classifier
● where VNB denotes the target value output by the naive Bayes classifier.
● To summarize, the naive Bayes learning method involves a learning step in which
the various P(vj) and P(ai|vj) terms are estimated, based on their frequencies over
the training data.
● The set of these estimates corresponds to the learned hypothesis.
● This hypothesis is then used to classify each new instance by applying the rule in
this Equation

48. ● One interesting difference between the naive Bayes learning method
and other learning methods we have considered is that there is no
explicit search through the space of possible hypotheses
● Instead, the hypothesis is formed without searching, simply by
counting the frequency of various data combinations within the
training examples

49. An Illustrative Example
Let us apply the naive Bayes classifier to a concept learning
problem we considered during our discussion of decision tree
learning:
classifying days according to whether someone will play tennis

51. ● Here we use the naive Bayes classifier and the training data from this table to
classify the following novel instance:
(Outlook = sunny, Temperature = cool, Humidity = high, Wind = strong)
● Our task is to predict the target value (yes or no) of the target concept Play
Tennis for this new instance.
● To fit the current task, the target value VNB is given by

52. ● Notice in the final expression that ai has been instantiated using the particular
attribute values of the new instance.
● To calculate VNB we now require 10 probabilities that can be estimated from the
training data.
● First, the probabilities of the different target values can easily be estimated
based on their frequencies over the 14 training examples

53. ● Similarly, we can estimate the conditional probabilities. For example, those for
Wind = strong are
● Using these probability estimates and similar estimates for the remaining
attribute values, we calculate VNB
● Thus, the naive Bayes classifier assigns the target value PlayTennis = no to this
new instance, based on the probability estimates learned from the training data.

54. AN EXAMPLE: LEARNING TO CLASSIFY TEXT

55. BAYESIAN BELIEF NETWORKS
● The naive bayes classifier assumes that all the variables are conditionally
independent given the value of the target variable
● Bayesian belief networks allow stating conditional independence
assumptions that apply to subsets of the variables
● It is an intermediate approach that is less constraining than the global
assumption of conditional independence made by the naive Bayes
classifier, but more tractable than avoiding conditional independence
assumptions altogether

56. ● In general, a Bayesian belief network describes the probability distribution
over a set of variables.
● Consider an arbitrary set of random variables Yl . . . Yn, where each variable
Yi can take on the set of possible values V(Yi).
● We define the joint space of the set of variables Y to be the cross product
V(Yl) x V(Y2) x . . . V(Yn).
● The joint probability distribution specifies the probability for each of the
possible variable bindings for the tuple (Yl . . . Y,).
● A Bayesian belief network describes the joint probability distribution for a
set of variables.

57. Conditional Independence
● Let us understand the idea of conditional independence
● We say that X is conditionally independent of Y given Z if the probability
distribution governing X is independent of the value of Y given a value for
Z; that is, if
● We commonly write the above expression in abbreviated form as
● P(X|Y, Z) = P(X|Z).

58. ● The definition of conditional independence can be extended to sets of variables as well.
● We say that the set of variables X1 . . . Xl is conditionally independent of the set of variables
Y1 . . . Ym given the set of variables Z1 . . . Zn, if
● There is relation between this definition and our use of conditional independence in the
definition of the naive bayes classifier
● The naive Bayes classifier assumes that the instance attribute A1 is conditionally independent
of instance attribute A2 given the target value V.
● This allows the naive Bayes classifier to calculate P(Al, A21V) in Equation
as follows

59. Representation
● A Bayesian belief network (Bayesian network for short) represents the
joint probability distribution for a set of variables.

60. FIGURE Explanation
● A Bayesian belief network. The network on the left represents a set of
conditional independence assumptions.
● In particular, each node is asserted to be conditionally independent of its non
descendants, given its immediate parents.
● Associated with each node is a conditional probability table, which specifies
the conditional distribution for the variable given its immediate parents in
the graph.
● The conditional probability table for the Campfire node is shown at the right,
where Campfire is abbreviated to C, Storm abbreviated to S, and
BusTourGroup abbreviated to B.

61. ● The figure represents the joint probability distribution over the boolean
variables Storm, Lightning, Thunder, ForestFire, Campfire, and
BusTourGroup.
● In general, a Bayesian network represents the joint probability
distribution by specifying a set of conditional independence assumptions
(represented by a directed acyclic graph), together with sets of local
conditional probabilities.
● Each variable in the joint space is represented by a node in the Bayesian
network.

62. ● For each variable two types of information are specified.
○ First, the network arcs represent the assertion that the variable is conditionally independent
of its nondescendants in the network given its immediate predecessors in the network.
○ Second, a conditional probability table is given for each variable, describing the probability
distribution for that variable given the values of its immediate predecessors.
● The joint probability for any desired assignment of values (y1, . . . , yn,) to the tuple of network
variables (Y1 . . . Yn,) can be computed by the formula
where Parents(Yi) denotes the set of immediate predecessors of Yi in the network.

63. Illustration
● To illustrate, the Bayesian network in Figure represents the joint probability
distribution over the boolean variables Storm, Lightning, Thunder, Forest Fire,
Campfire, and BusTourGroup. Consider the node Campfire.
● The network nodes and arcs represent the assertion that Campfire is conditionally
independent of its nondescendants Lightning and Thunder, given its immediate parents
Storm and BusTourGroup.
● This means that once we know the value of the variables Storm and BusTourGroup, the
variables Lightning and Thunder provide no additional information about Campfire.
● The right side of the figure shows the conditional probability table associated with the
variable Campfire.

64. ● The top left entry in this table, for example, expresses the assertion that
P(Campfire = TruelStorm = True, BusTourGroup = True) = 0.4
● Note this table provides only the conditional probabilities of Campfire given its parent
variables Storm and BusTourGroup.
● The set of local conditional probability tables for all the variables, together with the
set of conditional independence assumptions described by the network, describe the full
joint probability distribution for the network.

65. Inference
● We might wish to use a Bayesian network to infer the value of some target variable (e.g.,
ForestFire) given the observed values of the other variables.
● Given that we are dealing with random variables it will not generally be correct to assign the
target variable a single determined value.
● What we really wish to infer is the probability distribution for the target variable, which
specifies the probability that it will take on each of its possible values given the observed
values of the other variables.
● This inference step can be straightforward if values for all of the other variables in the
network are known exactly.
● In the more general case we may wish to infer the probability distribution for some variable
(e.g., ForestFire) given observed values for only a subset of the other variables (e.g., Thunder
and BusTourGroup may be the only observed values available).
● In general, a Bayesian network can be used to compute the probability distribution for any
subset of network variables given the values or distributions for any subset of the remaining
variables.

66. Learning Bayesian Belief Networks
● Several different settings for this learning problem can be considered.
○ First, the network structure might be given in advance, or it might have to be
inferred from the training data.
○ Second, all the network variables might be directly observable in each training
example, or some might be unobservable.
● In the case where the network structure is given in advance and the
variables are fully observable in the training examples, learning the
conditional probability tables is straightforward.
● In the case where the network structure is given but only some of the
variable values are observable in the training data, the learning problem
is more difficult.

67. Example

70. EM(Estimation/Expectation–Maximization) ALGORITHM
● In many practical learning settings, only a subset of the relevant instance features
might be observable
● Many approaches have been proposed to handle the problem of learning in the presence
of unobserved variables
● if some variable / is sometimes observed and sometimes not, then we can use the cases
for which it has been observed to learn to predict its values when it is not
● The EM algorithm a widely used approach to learning in the presence of unobserved
variables
● The EM algorithm can be used even for variables whose value is never directly observed,
provided the general form of the probability distribution governing these variables is
known
● The EM algorithm has been used to train Bayesian belief networks
● The EM algorithm is also the basis for many unsupervised clustering algorithms

71. Estimating Means of k Gaussians
● Consider a problem in which the data D is a set of instances generated by
a probability distribution that is a mixture of k distinct Normal
distributions

72. ● This problem setting is illustrated in Figure for the case where k = 2 and
where the instances are the points shown along the x axis.
● Each instance is generated using a two-step process.
● First, one of the k Normal distributions is selected at random.
● Second, a single random instance xi is generated according to this selected
distribution.
● This process is repeated to generate a set of data points as shown in the
figure.
● To simplify our discussion, we consider the special case where the selection
of the single Normal distribution at each step is based on choosing each with
uniform probability,

73. ● We would like to find a maximum likelihood hypothesis for these means;
that is, a hypothesis h that maximizes p(Dlh).
● It is easy to calculate the maximum likelihood hypothesis for the mean of
a single Normal distribution given the observed data instances x1, x2, . . .
xm drawn from this single distribution
● We already know that the maximum likelihood hypothesis is the one that
minimizes the sum of squared errors over the m training instances

74. ● In this case, the sum of squared errors is minimized by the sample mean
● Our problem here, however, involves a mixture of k different Normal distributions, and we
cannot observe which instances were generated by which distribution.
● Thus, we have a prototypical example of a problem involving hidden variables.
In the example of Figure we can think of the full description of each
instance as the triple (xi,zi l , ziz), where xi is the observed value of
the ith instance and where zil and zi2 indicate which of the two
Normal distributions was used to generate the value xi.
● In particular, zij has the value 1 if xi was created by the jth Normal distribution and 0
otherwise.
● Here xi is the observed variable in the description of the instance, and zil and zi2 are
hidden variables.

75. ● If the values of zil and zi2 were observed, we could use the equation
to solve for the means p1 and p2. Because they are not, we will instead use the EM algorithm.
● Applied to the problem of estimating the two means for Figure , the EM algorithm first
initializes the hypothesis to h = (PI, p2), where p1 and p2 are arbitrary initial values.
● It then iteratively re-estimates h by repeating the following two steps until the procedure
converges to a stationary value for h.

76. ● Let us examine how both of these steps can be implemented in practice.
● Step 1 must calculate the expected value of each zij.
● This E[zij] is just the probability that instance xi was generated by the jth Normal
distribution

78. ● The above algorithm for estimating the means of a mixture of k Normal
distributions illustrates the essence of the EM approach:
○ The current hypothesis is used to estimate the unobserved
variables, and the expected values of these variables are then
used to calculate an improved hypothesis.
○ It can be proved that on each iteration through this loop, the EM
algorithm increases the likelihood P(Dlh) unless it is at a local
maximum.
○ The algorithm thus converges to a local maximum likelihood
hypothesis for